Image segmentation is a branch of digital image processing that performs the task of categorizing, or classifying, the picture elements of a digital image as belonging or associated with one or more class types. For medical imaging applications, it is common that image segmentation is performed on the voxels (volume element) of a 3-dimensional image data set with the classification types relating to anatomical structure. In thoracic medical images, it is convenient to segment the image voxels into classes such as bone, lung parenchyma, soft tissue, bronchial vessels, blood vessels, etc. There are many reasons to perform such a task, such as surgical planning, treatment progress, and patient diagnosis.
Of particular interest is the image segmentation approach generally known as region growing. Starting with a seed point, i.e., a voxel position that is known to be part of a particular class type, a region of voxels, often contiguous in nature, is grown or otherwise developed about the seed point. The region growing process progresses until a terminating condition is satisfied, e.g., no more voxels that meet suitable criteria are found, or a predetermined number of voxels have been visited, etc.
Conventional segmentation approaches that begin with a seed point can work acceptably for some types of organ segmentation problems. However, because of its relatively complex tissue structure, the liver can be difficult to segment with conventional approaches.
One conventional technique is described in a paper entitled “An iterative Bayesian approach for nearly automatic liver segmentation: algorithm and validation” in International Journal of Computer Assisted Radiology and Surgery, Volume 3, Number 5, November 2008 by Freiman et al. These authors describe an algorithm for segmenting the liver organ in thoracic CT (computed tomography) medical volume images. This algorithm starts with the user of a PACS (Picture Archive and Communication System) supplying a seed point within the liver organ, within a displayed axial slice of a thoracic CT volume image. The automated processing includes performing a voxel value based classification step to define a first segmentation map. A series of morphological operations are performed on the first segmentation map to produce a modified segmentation map. A geodesic active contour algorithm is used to process the modified segmentation map to produce a final liver organ segmentation map.
In the method described by Freiman et al., the seed point, supplied by the user, is used to sample the local voxel values within a rectangular region about the seed point to determine the intensity range of values that correspond to the liver organ. The range of voxel value intensities corresponding to the liver organ are determined by assuming a generally Gaussian distribution of pixel or voxel values and then using the calculated mean and variance from the sampled region. The intensity range of values is then used to threshold the volume image and to produce a probability map, i.e. a map with values ranging from 0.0 to 1.0 that indicate the likelihood that the voxel belongs to the liver organ. Next, the probability map is spatially smoothed using a Maximum A Priori rule to produce the first segmentation map. The morphological operations that are applied include performing a largest connected components operation followed by hole filing and a morphological opening operation. The entire procedure described above is performed in a multi-resolution framework by operating on the lowest resolution component first and finishing with the highest resolution component.
The method described by Freiman et al. is used when the liver organ tissue is fairly homogeneous and is well differentiated from the surrounding background tissue in terms of voxel value. In practice, however, this is generally not the case. Instead, it has been found that noise levels in the CT image often make this type of approach unusable. Noise in a typical CT volume image can be similar in magnitude, i.e. standard deviation, to the expected difference in mean value from the liver organ tissue to the mean value of the background tissue. Where this is the case and the obtained volume image data representing the liver therefore fails to have a well-behaved Gaussian distribution, the method described by Freiman et al., yields disappointing results. Typically, such an algorithm tends to over-segment the liver, i.e. the final liver organ segmentation map includes substantial regions of non-liver tissue of the surrounding background.
Attempts to compensate for noise in the CT image do not appear to help the performance of the Freiman et al. approach. For example, the method described by Freiman et al. can be combined with a method described by Rudin, Osher, and Fatemi in the article “Nonlinear total variation based noise removal algorithms” published in Proceedings of the Eleventh Annual International Conference of the Center for Nonlinear Studies on Experimental Mathematics: Computational Issues in Nonlinear Science (1992), pp. 259-268. The noise removal algorithm (ROF filter) described by Rudin et al. is an iterative approach that removes variation, stochastic or structural, by successively iterating an energy minimization quantity. The ROF filter is designed to remove image variation, be it stochastically induced (noise) or structural in nature (relating to organ and tissue morphology). The ROF filter can be applied first to a volume image to remove the effects of noise, resulting in a noise filtered volume image. This noise filtered volume image can then be operated on by the Freiman algorithm to produce a liver organ segmentation map.
In practice, because boundaries between the liver and surrounding tissue can be fairly subtle in many cases, application of this combined approach using initial noise filtering with an ROF filter has not produced satisfactory results. Instead, the ROF filter often spatially smoothes one or more of the subtle boundaries between the liver and muscle tissues, between liver and spleen tissue, between liver and heart tissues, and between liver and stomach tissues. As a consequence, the resulting liver segmentation maps produced with the above mentioned combination approach can be over-segmented, often including substantial regions of non-liver tissue.
In general, typical imaging processing algorithms for segmentation of the liver and other organs in CT exam images show improved performance when a contrast agent has been administered to the patient. The contrast agent, applied within the blood stream, tends to augment the voxel value differences between anatomical structures. As a general rule, the greater the voxel difference between an organ and its surrounding background, the better the resulting organ segmentation when using conventional segmentation algorithms. In the case of the liver organ, high levels of contrast agent help to differentiate between healthy liver parenchyma tissue and unhealthy tissue. For these cases, the organ segmentation task is complicated. However, most conventional liver organ segmentation algorithms are not suitable for CT exams in cases where no contrast agent had been administered to the patient, due to the inherently poor liver-to-background voxel difference. In practice, the liver-to-background voxel differences can even be smaller than the noise magnitude present in the CT image data itself.
Thus, it can be appreciated that there is a need for an improved image processing method for segmentation of the liver and other organs.